Transforming the Customer Experience with Artificial Intelligence

Customer Experience has been gaining increasing space in companies in all segments as an area responsible for organizing and leading the necessary changes in organizations, in order to improve customers' perception of the value of the product or service. Leading companies in their sectors have already realized this many years ago, but many other companies and people are understanding day after day the importance of paying attention to what the customer says, how they act and what they think about your product. There are several ways to work on Customer Experience in each business, and looking at this strategically ensures that best practices are adapted to each business.

But, if on the one hand Customer Experience has been worked on for years, Artificial Intelligence (AI), in turn, only became popular and gained due attention in our daily lives last year. At least, for the masses. The advances demonstrated by products such as ChatGPT, Dall-e, Bard and others have made people (thus, customers) increasingly interested in what this revolutionary technology is capable of improving their lives. Whether creating text or images, answering questions or helping to make life simpler and more productive, the potential of Artificial Intelligence to change our society is already compared to the invention of the internet itself, and may even be greater than it. This is a belief of Sundar Pichai, CEO of Google, for example, but also of several other technology and business leaders today.

According to research from Zendesk, customers are already concerned about consuming products from companies that use AI to improve their lives. According to the report,

“About 60% believe companies are failing when it comes to creating seamless experiences between physical locations and their corresponding websites. Slightly more – 63% – think AI can fill these gaps, and almost two-thirds of respondents want the technology to allow their personal information to be available to every employee at the company they work for”.

Therefore, worrying about including Artificial Intelligence in the development of your business is crucial for those who want to stand out and advance in the face of the competition.

Understanding Artificial Intelligence

But what is artificial intelligence?

Artificial Intelligence (AI) is a field of computer science that is dedicated to the development of systems and algorithms that seek to create machines capable of performing tasks that, when done by human beings, require intelligence. These tasks include learning, problem solving, pattern recognition, natural language understanding, and decision making, among others.

The evolution of AI over time can be divided into several phases:

Prehistory and Foundations (Before 1950): In this initial phase, the first steps were taken with the idea of creating machines capable of imitating human thought. Alan Turing, in 1950, developed the "Turing Test", a landmark in the field of AI that would evaluate a machine's ability to exhibit intelligent behavior indistinguishable from that of a human being.

First Wave of AI (1950s-1960s): During this phase, there was great optimism in the AI research community, with the belief that AI could soon solve complex problems. However, progress has been limited due to a lack of computing power and limited understanding of machine learning algorithms.

AI Winter (1970s-1980s): In this period, there was a decline in funding and interest in AI due to a lack of progress and unmet expectations. Many researchers have abandoned the field.

Second Wave of AI (1980s-1990s): With the development of more advanced technologies, such as more powerful computing and machine learning techniques, AI began to experience a renaissance. Expert systems, artificial neural networks and other approaches have gained prominence.

Machine Learning Era (2000s onwards): AI began to focus more on machine learning, especially deep learning algorithms. This has enabled significant advances in pattern recognition, natural language processing, computer vision and other areas.

Current AI (2020s onwards): AI is increasingly integrated into our lives. It is used in virtual assistants, self-driving cars, medical diagnosis, data analysis, video games and many other fields. The continued development of more powerful hardware and expanding data sets are driving new advances in AI.

One of the main concepts to understand when we talk about AI is the concept of neural networks. Neural networks are crucial components of AI that mimic the functioning of the human brain. They consist of layers of interconnected neurons, processing information and adjusting connection weights during training. This enables neural networks to identify patterns and predict outcomes based on data, making them essential in applications such as natural language processing and computer vision. Deep neural networks, with multiple layers of neurons, are particularly effective, allowing the learning of complex representations of input data.

A practical example of how an AI can learn using neural networks is handwriting recognition. Suppose we want to create a system capable of recognizing handwritten letters. In this case, we can use a convolutional neural network (CNN), a type of neural network designed specifically for processing image data.

Initially, we feed the network with a large dataset containing images of handwritten letters and the corresponding letters. During training, the neural network adjusts its connection weights to learn relevant features, such as edges, curves, and strokes, that distinguish letters. As more examples are presented, the network improves its generalization ability.

After training, the neural network is able to analyze images of unknown handwritten letters and classify them correctly. This type of handwriting recognition is used in various applications, such as document scanning, character recognition on mobile devices and even in speech recognition systems that transcribe what is said into text, all thanks to learning obtained through neural networks.

Conversational Artificial Intelligences

Conversational Artificial Intelligences, such as chatbots and virtual assistants, are AI systems designed to interact with humans in a natural and understanding way through conversations. They have gained significant prominence in recent years due to advances in natural language processing and machine learning.

Chatbots are computer programs that can answer questions, perform specific tasks, or offer information based on predefined rules or through machine learning. They are often used on websites, messaging apps, and customer service to automate interactions with users.

Virtual assistants, on the other hand, are more advanced chatbots that integrate a wide range of functionality and learn from experience. They are capable of performing complex tasks such as booking flights, scheduling appointments, searching for information on the internet and even controlling smart home devices. Some popular examples of virtual assistants include Apple's Siri, Google Assistant, and Amazon's Alexa.

To achieve their conversational capability, these systems use natural language processing (NLP) techniques to understand and interpret human language. They process and analyze the user's text or speech, extract meaning and generate appropriate responses. Machine learning plays a crucial role, allowing these AIs to improve their ability to understand and adjust their responses based on previous interactions.

AI and Customer Service

As you have seen so far, there are a multitude of situations and uses for artificial intelligence, and many of them directly affect the customer experience. Chatbots and virtual assistants are already being used to facilitate customer service and sales, as well as other processes throughout the customer journey. Today, we see a huge number of chatbots working in customer relationships, but these, for the most part, are not built based on neural networks and machine learning. For the most part, they are created based on a decision tree. Therefore, it is very common for the experience with these chatbots to be very frustrating for customers, which is not the case with chatbots based on conversational artificial intelligence. But what are the differences?

Chatbots Based on Decision Trees:

• Predefined Rules: Decision tree-based chatbots operate based on a set of predefined rules and paths. Your answers are limited to the options available in the decision trees.

• Standardized Answers: They offer standard answers and often cannot handle questions or situations outside the scope of their decision trees well.

• Easy Initial Implementation: Decision tree chatbots are relatively easy to implement, especially for simple, routine interactions.

• Less Flexibility: They have less flexibility in understanding natural language and may not be able to effectively deal with variations in how users phrase questions.

Neural Network Artificial Intelligence Chatbots:

• Machine Learning: Neural network chatbots use machine learning techniques, such as deep neural networks, to process natural language and learn from large volumes of data. They can understand and generate more flexible and contextual responses.

• Flexibility in Understanding: These chatbots have the ability to understand nuances in language, including slang, regional accents and complex sentences. They can provide responses closer to the human communication style.

• Continuous Learning: Neural network chatbots can improve over time as they are exposed to more interactions and data, making them more effective at complex and varied tasks.

• Adapting to Change: They are more flexible and can adapt to changes in user preferences and customer service demands.

In short, neural network chatbots are more advanced and flexible due to the use of machine learning techniques, which allows them to understand and respond in a more natural and adaptive way. On the other hand, chatbots based on decision trees are simpler and more direct, being appropriate for use cases with limited and well-defined interactions. Choosing between these approaches depends on the specific needs of a chatbot project and expectations regarding the level of complexity and personalization of user interaction.

For example, a cafeteria that wants to implement a chatbot to generate orders made by customers, can use one based on a decision tree very efficiently, since the options available to the customer are limited to the cafeteria's menu. The answers are unlikely to deviate from the name of the dish in the “food” options; and “water”, “coca-cola” or “juice” in the drink options. For the cafeteria, at this stage of the customer journey, advanced conversational AI may not make sense.

But, a professional training school that has different courses may have to deal with a question like: “I'm not sure whether to take the marketing or sales course. The sales one lasts only 1 week, the marketing one lasts 4 and seems to cover a sales module, and the price difference is not that big. What is the best option for me?” This question, if asked to a human, would generate an important context demand to be analyzed and would be complex to answer. However, a conversational AI based on neural networks could generate a contextualized response of the differences and purposes of each of the courses and offer a response based on other previous interactions that generated better conversion results, for example. The AI's response would most likely be more contextual and elaborate than that of a human professional.

A great example of Artificial Intelligence used in a sales process comes from Amazon's Alexa, through which it is possible to add items to your shopping cart and even complete the purchase through the virtual assistant, which already knows your payment details and the delivery address.

Another example from Amazon is the ability for Alexa to read books you purchased on your Kindle. You may be doing a routine activity, like washing the dishes or cleaning the house, while she reads your book to you. It is capable of interpreting text information and transforming it into audio with a relative similarity to natural language.

Advantages and challenges of using chatbots in customer service.

Using chatbots in customer service offers several advantages, but it also presents challenges that need to be considered. Here are some of the main advantages and challenges:

Benefits:

1. 24/7 Availability: Chatbots can provide customer support at any time of the day or night, ensuring service outside of business hours and across time zones.

1. Efficiency: Chatbots can handle multiple queries simultaneously, reducing wait times and improving customer response times.

1. Cost Savings: Automating customer service can reduce operational costs, as fewer human agents are needed to handle routine tasks.

1. Consistent Responses: Chatbots provide consistent, standardized responses, ensuring that all customers receive the same accurate information.

1. Machine Learning: With machine learning, chatbots can improve their performance over time as they accumulate more data and experience.

1. Languages: Chatbots based on neural networks can communicate in any user language very naturally. A chatbot based on ChatGPT can interpret a huge variety of different languages and even regional differences (such as Portuguese from Portugal or Brazil).

Challenges:

1. Limitations in Understanding: Chatbots may have difficulty understanding nuances in language and complex questions, leading to inadequate responses. For example, if the audience uses a lot of slang and abbreviations, it may be more complex for the AI to understand what is being said.

1. Impersonal Interactions: Compared to human agents, chatbots can seem impersonal and may not be as effective in emotional or complex situations.

1. Training and Maintenance: Developing and maintaining effective chatbots requires investment in training, updating data and constant adjustments.

1. Privacy and Security: Protecting customer data is paramount, and chatbots need to be designed to ensure the security of personal information.

1. Limited Natural Language Recognition: Chatbots may have difficulty understanding regional accents, slang, and less common languages.

1. Customer Acceptance: Some customers may prefer interactions with humans and may resist the use of chatbots, especially in more complex situations. Here, the most important thing is to know your audience very well.

In summary, chatbots offer efficiency and 24/7 availability in customer service, but they also face challenges related to natural language understanding and the ability to provide an appropriate level of empathy and support in complex situations. Therefore, the decision to implement chatbots in customer service must be balanced with the needs and expectations of customers and the ability to maintain and continuously improve these systems.

At sonata.cx we are developing a chatbot based on chatGPT that will help customers better understand their demands and how our consultancy can help them.

AI and Qualitative Data Analysis

Another way AI can help the customer experience is through qualitative data analysis. Companies that deal with a huge amount of customer contact data, or opinion surveys, can benefit from AI data processing to derive insights and learnings much faster than would be possible with human analysis alone. Data like NPS survey responses can provide very useful information about reasons for detraction or promotion. Recently, it is work for a large client at sonata.cx , we used Artificial Intelligence to perform analysis on a huge amount of NPS data provided by customers to find the right learnings about the product usage experience, which allowed us to explore the answers based on new user research and validate learning.

Another way to use AI to analyze large amounts of data is to provide it with a vast history of interactions between customers and customer service agents and ask it to create a FAQ (frequently asked questions). It is not only capable of determining what the most frequently asked questions are, it can organize them from largest to smallest, as well as interpreting what the right answers are and designing the necessary processes to be followed based on the agents' response history. With a few simple commands, AI is capable of providing you with a complete FAQ and very well-designed processes that can be used to train and provide feedback to your customer service team, while helping to reduce the contact rate, thus increasing the scalability of the operation.

Additionally, there are other ways AI is positively impacting customer feedback analysis:

• Automatic Classification: AI can automatically classify customer reviews into relevant categories, identifying specific topics, sentiments (positive, negative, neutral), and themes. This helps companies identify areas that need immediate attention.

• Detection of Hidden Insights: AI is capable of discovering hidden insights in customer feedback that may not be evident at first glance. This helps companies make informed decisions based on previously untapped information.

• Trend Analysis: AI can track and analyze trends over time, allowing businesses to track changes in customer preferences and adapt their strategies accordingly.

• Real-Time Feedback: AI systems can analyze feedback in real-time, allowing companies to act quickly to resolve issues or seize opportunities.

• Personalizing the Customer Experience: Based on feedback analysis, AI can help companies personalize the customer experience, offering products, services and interactions that are more aligned with individual preferences.

• Performance Metrics: AI can generate performance metrics based on customer feedback, allowing companies to assess the impact of their actions and strategies on customer satisfaction.

• Churn Prediction: AI can help predict customers who are at risk of canceling their services, allowing companies to take proactive measures to retain these customers.

• Multichannel Feedback: AI is able to integrate feedback from multiple channels, including social media, emails, chat, surveys and more, providing a more holistic view of the customer experience.

In short, AI is transforming customer feedback analysis, empowering companies to make informed decisions, improve customer satisfaction, identify business opportunities and improve operational efficiency. This results in more positive experiences for customers and, at the same time, contributes to the success of companies.

One of the best examples of using AI to personalize customer experience is Netflix, which basically has a home screen for each customer, based on their history and preferences. It is unlikely that two people will see the same home screen and the same recommendations when they open Netflix.

None of this, however, should be done alone, entrusting everything to AI. Your team must be prepared to help the AI reach these answers. Well-trained professionals must continually teach AI again, and use their learnings to generate real changes in their companies. Another important task is to clean the AI base with answers or information that are true and aligned with the company's objectives. There is no point in feeding the AI database with responses from service agents who did not follow the processes correctly or did not give the right answers. This can, in fact, hinder much more than help your company to use AI.

Preparing for the AI Revolution in CX

As Artificial Intelligence (AI) continues to advance and become increasingly integrated across industries, it is already clear that companies face a revolution that will transform the way they operate, compete and serve their customers. To prepare for this revolution, companies must adopt a strategic approach that includes developing AI capabilities, defining clear implementation strategies, and establishing ethical and responsible practices. Furthermore, it is essential to create a culture of innovation and continuous adaptation to make the most of AI's potential and maintain competitiveness in a constantly evolving business landscape. This paragraph explores the key steps companies can take to effectively prepare for the AI revolution and seize the opportunities it offers.

To adopt Artificial Intelligence (AI) effectively, companies must consider several key strategies:

1. Understanding the Business Need: Start by identifying the business problems or opportunities that AI can solve. Clearly define objectives and desired outcomes to ensure AI implementation is targeted and focused. It's very easy to get lost in so much news and one thing companies shouldn't do is waste resources and time to beat the competition.

1. Development of AI Skills: Invest in training teams with knowledge in AI. This may include hiring experts, in-house training, and partnering with companies specializing in AI. sonata.cx has increasingly specialized in studying and learning about Artificial Intelligence and how to apply it in everyday life. We can help you from consultancy with implementation, to training the team in the skills and abilities necessary to work well with AI.

1. Data Collection and Preparation: AI depends on high-quality data. Therefore, establish a process for collecting, cleaning, and organizing relevant data. Ensure data is available and accessible to power AI models. Here, once again, well-prepared human capital is crucial to success.

1. Selecting the Right Technology: Choose the AI tools and platforms that best meet your company's needs. This may include machine learning solutions, natural language processing, computer vision, among others.

1. Model and Algorithm Development: Create custom AI models or use pre-trained models depending on requirements. Test and tweak these models to ensure they produce the results you want.

1. Ethics and Transparency: Implement ethical AI practices and promote transparency. Ensure data collection and use are ethical and AI models are explainable.

1. Integration with Existing Processes: Integrate AI with existing business processes for efficiency. This may involve creating APIs for legacy systems or adopting AI solutions into CRM and ERP systems.

1. Continuous Learning: AI is dynamic and requires continuous learning. Update models regularly and be prepared to adapt AI to changing business needs.

1. Testing and Validation: Conduct rigorous testing to ensure AI models work as expected. Use performance metrics to evaluate AI effectiveness. This moment can be frustrating because the results are still not close to what is expected, but remember that machine learning models improve over time and the gain is exponential.

1. Culture of Innovation: Create a culture of innovation that encourages experimentation and creative use of AI across the organization. Start small and accept mistakes. In fact, encourage them, because making quick and small mistakes is one of the best ways to learn and develop a good culture.

1. Collaboration with Human Experts: Recognize that AI is a tool that complements human work. Drive collaboration between AI and human experts for better results. Once again, sonata.cx can help you.

1. Impact Measurement: Establish clear metrics to measure the impact of AI on business objectives. Regularly track performance and adjust strategies as needed.

These are not universal rules and nothing here is written in stone, but by adopting these strategies, companies can harness the potential of AI to improve efficiency, innovate, better serve customers and maintain a competitive advantage in an increasingly driven by data and technology. Each business will have different needs.

The role of leadership in transforming CX with AI.

Leadership plays an absolutely crucial role in the process of incorporating Artificial Intelligence (AI) into everyday business. Enabling the organization's leaders to face challenges, such as dealing with errors, adapting to rapid changes in direction and assimilating constant and frequent feedback, often in real time, is extremely important. Furthermore, the ability to question established beliefs and be willing to challenge one's perspective is fundamental. These skills are essential for a leader who aspires to lead their teams to achieve excellence in the use of AI, aiming to improve the customer experience. As AI becomes an increasingly integrated part of operations, leaders play a critical role in establishing a culture of continuous learning, innovation and agile adaptation, ensuring AI is an ally in the pursuit of customer satisfaction and success .

Dealing with AI means continually learning and adapting to the evolution of a field of computer science that changes drastically in very fast cycles. This ability to learn and adapt is crucial to obtain good results in a constant and permanent way, because not only will the processes and companies evolve, but the algorithms and tools themselves will be constantly evolving very quickly, as we have already seen in the first paragraphs. The very technology you create today will evolve in the future (machine learning) and you need to be able to not only follow this evolution, but to be ahead and leading the way to where it should evolve, based on your business objectives .

Challenges and Ethics of AI in CX

The intersection between Artificial Intelligence (AI) and customer experience (CX) presents important challenges that need to be managed sensitively and ethically. One of the main challenges lies in algorithmic bias, where AI systems can reflect biases present in training data, leading to discrimination or unfair treatment of customer groups. Overcoming this bias is crucial to ensuring CX is fair and impartial for all customers.

Additionally, collecting and using customer data for personalization can raise privacy concerns. Companies must prioritize obtaining consent and adopt safe and ethical practices to ensure customer data is treated responsibly.

The transparency and interpretability of AI systems are also critical points. Customers have the right to understand how decisions are made, making it essential to make AI processes more transparent and explainable.

Automating customer service processes through chatbots and virtual assistants can bring efficiencies, but it's important to balance this automation with the ability to deliver authentic, empathetic human interactions when needed. Furthermore, companies must be responsible for the actions of their AI systems, including correcting errors and responding to unforeseen situations in an ethical and effective manner. Another important consideration is the training and qualification of AI agents, ensuring they are trained ethically and behave appropriately when interacting with customers.

Finding the balance between personalizing the customer experience and the need for customers to have control over their preferences and personal data is a complex challenge. This requires an ethical approach to ensure personalization does not compromise privacy.

Finally, AI must be continually trained and improved ethically, taking into account feedback and insights to avoid amplifying biases and making harmful decisions.

Addressing these challenges and adhering to ethical principles when implementing AI in CX is critical to building lasting customer relationships, maintaining trust, and meeting expectations for a fair and satisfying customer experience.

The Future of CX with Artificial Intelligence

The future of customer experience (CX) with Artificial Intelligence (AI) is promising, with emerging trends and developments that promise to further transform the way companies interact with and serve their customers. A growing trend is hyper-individualized personalization, where AI uses contextual and historical data to precisely adapt interactions, delivering tailored experiences for each customer. Additionally, AI is becoming more natural in communication, with chatbots and virtual assistants capable of understanding nuances in language and providing contextual responses, making interactions more human.

The vision for AI's potential long-term impact on customer experiences is to create deeper, more meaningful relationships between companies and their customers. With AI playing a central role in automating routine tasks, human agents can focus on higher-value interactions and dealing with complex and emotional situations. This will lead to an increase in operational efficiency and a significant improvement in customer satisfaction. Furthermore, AI will allow companies to predict customer needs and act proactively, anticipating problems before they occur.

To stay ahead in this constantly evolving landscape, companies must adopt a customer-centric approach, where ethics and transparency in the use of AI are fundamental. They must invest in training and developing AI skills for their teams, promoting a culture of innovation and continuous learning. Furthermore, collaboration between humans and AI will be essential, leveraging the best of both capabilities to create exceptional customer experiences. As AI becomes more sophisticated, continued attention to data security and customer privacy will be critical to maintaining customer trust and loyalty. In summary, the future of CX with AI is promising, but it requires an ongoing commitment to ethics, innovation, and excellence in delivering exceptional customer experiences.

Conclusion

Implementing AI in Customer Experience will require new skills and competencies (both technical and behavioral) for leaders and specialists who need to be adapted to constant changes and a huge diversity of scenarios and possibilities.

One of the best and main ways to accelerate this learning is by hiring professionals who are already at the forefront of these fields. However, because everything is still so new, good professionals are already very well employed, thank you. Therefore, hiring a consultancy could be the difference you need to systematically address everything discussed here.

Weighing the development of technology, its use in your organization and the impact on human lives (both employees and customers) is a job that requires a careful and empathetic look from leadership, while it needs to be faithful to the purpose of improving its products, processes and services in a powerful way through AI.

sonata.cx can help you at this time. Send us a message and we will be happy to assist you.

References

Artificial intelligence in customer service: how to use it? ( zendesk.com.br )

The future of the job market: impact on jobs, skills and wages | McKinsey

AI: an opportunity amid the crisis ( pwc.com.br )

IBM Study: 41% of companies in Brazil have already actively implemented Artificial Intelligence in their businesses - IBM Comunica